Application of machine learning and emotional fluctuation system in game quality evaluation and prospective players prediction
Previous studies revealed that the players' emotional fluctuation is associated with and may be used to evaluate the quality of the game. This study aims at determining how the changes in a variety of emotional factors would reflect the quality of the game. Galvanic Skin Response GSR, an emotional-aware UI, was used to gain data from volunteers while they were playing games. The linear regression model was used to predict the prospective players in the game series. Results suggested that some emotion factors like fluent frequency have positive influence of the game quality while more emotion factors such as biggest wave and interval have negative influences.
2018 IEEE 4th International Conference on Computer and Communications, ICCC 2018
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Li, Tianshu; Meng, Yu; Hu, Hanxing; and Zhang, Changjiang, "Application of machine learning and emotional fluctuation system in game quality evaluation and prospective players prediction" (2018). Kean Publications. 1443.